The intention in the above blog post isn't to suggest that we discard detailed research papers. It's just thinking about additional mediums we can use to make papers more accessible(a good example is distill.pub and paperswithcode). As mentioned in other threads below, PDF can be a limiting medium.
distill.pub heavily favors fancy and interactive visualization over actually meaningful research content. This is not to say that the research publicized on distill.pub is not meaningful, but that it is biased to research that can have fancy visualizations. So you end up seeing a lot of tweakable plots, image-augmentations, and attention weights visualizations. It is also further biased towards research groups that have the resources to create a range of D3 plots with sliders, carved out of actual research time.
For instance, I don't think BERT could ever make it into a distill.pub post. Despite completely upending the NLP field over the last 2 years, it has no fancy plots, multi-headed self-attention is too messy to visualize, and its setup is dead simple. You could maybe have one gif explaining how masked language modeling works. The best presentation of the significance of BERT is "here is a table of results showing BERT handily beating every other hand-tweaked implementation for every non-generation NLP task we could find with a dead-simple fine-tuning regime, and all it had was masked language modeling."
To give another exmaple: I think it's one of the reasons why a lot of junior researchers spend time trying to extract structure from attention and self-attention mechanisms. As someone who's spent some time looking into this topic, you'll find a ton of one-off analysis papers, and next to no insights that actually inform the field (other than super-trivial observations like "tokens tend to attend to themselves and adjacent tokens).
I totally agree that additional media could be good. I got caught up on the "most papers could be compressed to < 50 lines" line and misunderstood the premise you were presenting.